WORKINGPAPER SERIES. Deepening Divides in Jobless Recovery Exploring the Demographic Factors Affecting Passage of Living Wage Ordinances

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1 POLITICAL ECONOMY RESEARCH INSTITUTE University of Massachusetts Amherst Deepening Divides in Jobless Recovery Exploring the Demographic Factors Affecting Passage of Living Wage Ordinances Robert Pollin Oren M. Levin-Waldman POLITICAL ECONOMY RESEARCH INSTITUTE Gordon Hall 418 N. Pleasant St, Suite A Amherst, MA, Telephone: (413) Facsimile: (413) peri@peri.umass.edu Website: WORKINGPAPER SERIES Number 82 88

2 Exploring the Demographic Factors Affecting Passage of Living Wage Ordinances Oren M. Levin-Waldman Graduate School for Public Affairs and Administration Metropolitan College of New York 75 Varick Street New York, NY (212) x2220

3 Exploring Demographic Factors Affecting Passage of Living Wage Ordinances Cities that have passed living wages have generally done so in response to a couple of things: First that the national minimum wage has failed to keep pace with inflation and thus is insufficient to enable low-wage workers to adequately support themselves. And second that municipalities have outsourced service jobs once performed by higher paid unionized workers to contractors whose employees are paid considerably less. To some extent, these two trends are well captured in the larger economic transformations that have plagued many of the nation s cities over the years: mainly the exodus of manufacturing based employment and its replacement with a service economy. Moreover, this has all occurred against the larger backdrop of rising income and wage inequality. Though there are clearly objective factors that may explain what grass roots living wage campaigns have been reacting to, what has not been addressed are the characteristics of those cities that have passed ordinances. That is, are there no other reasons for why some cities might be more likely to pass these ordinances than others? Invariably, there are a whole host of variables that go into a city s ultimate adoption of a living wage ordinance, most notably its political culture and the nature of its political regime. To explore that aspect is clearly beyond the parameters of this paper. At the same time there are other factors, most notably the structure of its labor market and the demographics of that labor market, that may predispose it more to the adoption of such ordinances. In other words, the political campaigns for living wages that occur at the local level are no doubt a response to the changing economic circumstances, which ultimately provides them with their strongest political appeal. But it is also possible that the characteristics of these cities that are more predisposed to 1

4 passing such ordinances also give these political appeals more traction. This obviously cannot be completely proven simply by comparing respective labor market characteristics, they are nonetheless suggestive, and for this reason greater exploration is warranted. In this paper, I examine some of those features on the basis of data from the Current Population Survey (CPS) by comparing cities that passed ordinances to those that did not. What I intend to show is the following: cities with certain demographics, particularly higher concentrations of immigrants from south of the American border, lower levels of educational attainment, more people in low wage industries, and higher rates of income inequality, appear to be more likely to pass living wage ordinances than those cities that do not have these demographics. This, of course, would raise the further question of what would be more important in explaining why some cities are more predisposed to passing ordinances over others. Is it the various demographic variables or the level of income inequality? And for that matter, what do these variables necessarily have to do with the efforts of local organizers to mobilize grass-roots campaigns in response? Do growing inequalities in urban labor markets create incentives for collective action to launch living wage campaigns? Data from the CPS cannot easily answer this last question, but to the extent that demographic variables may explain why some cities have greater income inequality than others, it perhaps answers the question in an indirect way. This is not to say, however, that these cities demographic factors are themselves the causes of living wage campaigns. Rather, growing income inequality, also perhaps related to certain demographic features, may create pressure for some collective action. That is, I want to suggest that cities that have passed ordinances have certain characteristics that perhaps do presuppose them to being exploited by these political 2

5 campaigns. At the same time, the specific demographics of labor markets in those cities that passed ordinances may also more easily lend themselves to the type of grassroots organizing of workers that many living wage campaigns appear to be predicated on. The data does make clear, however, that cities with large immigrant populations with low educational attainment are thus more likely to have sizeable labor markets at the low-end of the income distribution. As such, these cities may only offer greater opportunity for living wage campaigns to organize these workers, who, because they are at the bottom of the wage scale, also have greater incentive to join in living wage campaigns because they are in a position to benefit. To the extent that this can be shown from the data, it would also imply the converse: those cities with smaller populations that are likely to be working in the low-wage labor market are perhaps less likely to be organized because the potential for mobilization is also less. Also because there are higher percentages of people who are better positioned to earn better wages, there is less incentive to be supportive of such campaigns. In other words, growing income inequality in urban labor markets, particularly with specific demographic profiles, may, because of the nature of those profiles, effectively create incentives for collective action to launch living wage campaigns. Living Wages and Income Inequality? To the extent that living wage campaigns may be reacting to rising income inequality, it might then be assumed that there is reason to believe that living wage ordinances, as yet another labor market institution, may go a long way towards at least slowing the increase in income inequality. At a minimum, it should reduce income inequality at the bottom of the income distribution. In other words, aside from the question of fairness and economic justice, do 3

6 campaign organizers assume there to be a relationship between institutional settings, labor market structures and the adoption of redistributive policy interventions? Although this question has not specifically been addressed with regards to the living wage, there is some general support for the notion that wage policies can reduce income inequality. On this, some of the literature on the effects of labor market institutions such as unions and the minimum wage is quite informative. Inequality, in short, tends to be less in places with labor market institutions (Howell and Huebler 1999). But once such institutions begin to deteriorate, rising income inequality is by no means an unexpected outcome. Overall, the literature suggests that wage institutions in general specifically minimum wages and unions do make a difference in reducing income inequality because they effectively boost the wages of those at the bottom of the distribution. It is suggestive because it is based on the effect that the deterioration of these institutions has had on the income distribution, not necessarily because it has been demonstrated that the presence has resulted in less income inequality. Rather income inequality was actually found to have increased in places where labor market institutions like unions and the minimum wage have deteriorated (Freeman 1993; Lee 1997; Machin 1997). Different institutions also appear to affect men and women differently. Nicole Fortin and Thomas Lemieux (1997), for instance, found wage inequality among men to be affected by a decline in unions, while wage inequality specifically among women was affected by the decline in the minimum wage. Tom Palley (1999) found that between 1980 and 1997, the gini coefficient increased by 17.5 percent and that 40 percent of this increase was due to a decline in union density and 9 percent was attributable to a decline in the real value of the minimum wage. He suggests that 4

7 unions in particular check income inequality by promoting the creation of wage contours that effectively raise the wages of all those with similar skills within the same occupational categories. They also reduce income inequality by restraining inequality within firms. Within firm inequality has increased because of the declining strength of unions, which has only had spillover effects into the general labor market. Consequently, new wage contours are created that favor managerial and professional occupations. Similarly, Oren Levin-Waldman (2002) found that individuals were more likely to earn wages within a range of the statutory minimum wage in states with right-to-work laws (labor market institutions that effectively suppress wages) than states with high union density. Wages for those in high union density states tended to be higher than in right-to-work states. Just as there were institutions to boost wages, there were also institutions to suppress wages, thereby implying that institutions do make a difference. Centralized types of wage setting institutions lead to a more equal wage distribution, and when these institutions are in decline the wage distribution is more likely to become unequal (Piore 1995; Gordon 1996; DiNardo and Lemieux 1997; Galbraith 1998; Palley 1998; Lemieux 1998; Wallerstein 1999; Craypo and Cormier 2000). All this, then, implies a clear role for living wage ordinances as a tool for potentially reducing income inequality, not because they will significantly narrow the gap between the top and bottom, but because studies suggest that income inequality is less when the average income of those at the bottom rises at a faster rate than those at the top. According to Peter Gottschalk (1997), decreasing income inequality can be accounted for by greater income growth among those at the bottom. Income inequality actually increases when the growth of income is greater among those at the top than among those at the bottom, even though bottom incomes have 5

8 improved in absolute terms. While mean wages grew rapidly during the 1950s and 1960s the dispersion around the growing mean changed very little. So long as those at the bottom of the income distribution gained along with everyone else from secular growth in the mean, it was a foregone conclusion that poverty rates would be kept down. On the basis of data from the CPS between 1991 and 1998, Levin-Waldman (2001) found that when compared to New York, Philadelphia and Denver, income inequality in Baltimore actually decreased quite substantially, and in large part because the average incomes of families in the bottom quintile rose at a considerably higher rate than those in the top quintile. Compared to the nation as a whole, income inequality in New York City, as measured by the ratio of families in the top fifth of the income distribution to the bottom fifth of the distribution, was 22.9 the end of This compares to a ratio of 14.2 for the country as a whole. Whereas income inequality increased by 21.4 percent in the United States during the 1990s, it increased by 29.4 percent in New York City. Income inequality also increased by 36.8 percent in Denver and by as much as 64.3 percent in Philadelphia, where the top-to-bottom ratio was 20.2 at the end of In Baltimore, where the first Living Wage ordinance was passed in 1994, income inequality actually dropped by 23.4 percent. Similarly, income inequality also dropped by 6.4 percent in San Francisco, where various municipal authorities within the city have also passed Living Wage ordinances. Though it is not clear that Living Wage ordinances per se were responsible for these decreases, it is important to note that income inequality did drop in those two cities because the average income of those at the bottom increased by a higher percentage (26.7 percent in Baltimore and 19.8 percent in San Francisco) than the average incomes of those at the top (2.3 percent in Baltimore and 13.0 percent in San Francisco) (38-39). 6

9 Though the number of studies on the living wage have grown in recent years, still very little is known about just what its effects would be. Among the more theoretically comprehensive studies is one by David Neumark (2002). Neumark examines CPS data and attempts to look at the effects of the living wage by comparing changes in cities that have passed living wages with those that have not. Among his findings are that poverty in those cities that passed living wage ordinances was less than in those cities that did not pass such ordinances. The principal problem with this approach, of course, is that while correlations may be established with the living wage, causal relationships cannot be. At best, this approach only lends itself to inference, but it by no means establishes that poverty in those cities was less because they passed a living wage. Nevertheless, his findings also suggest that in those cities where living wage ordinances were passed there were also sizeable wage gains among low-wage workers, and that above the 10 th percentile level, there was no evidence of disemployment effects. Rather, there was evidence of positive employment effects between the 50 th and 75 th percentiles, which is also consistent with a substitution of higher-skilled workers. Evidence on employment effects, however, was weaker than evidence on wage effects (pp ). Neumark s findings, however, shouldn t be taken as dispositive. While he concludes poverty to be reduced in cities with living wages, the best that he can offer is a positive correlation. He cannot establish that poverty was in fact lower because of the living wage. More to the point, however, because his econometric model relies on a truncated sample of workers which excludes higher wage workers, there is the problem of sample selection bias. As Mark Brenner, Jeannette Wicks-Lim and Robert Pollin (2002) point out, data from the CPS simply cannot be used in this manner, because it does not capture the actual experiences of those cities that have adopted these ordinances. On the contrary, the CPS is individual level data 7

10 and Neumark s classification of workers as potentially covered may not be consistent with the reality. He classifies workers as potentially covered because they may be legally receiving wages as a function of these ordinances, but he provides no evidence that his assignment is consistent with the actual experiences of those cities that have implemented these ordinances. Living Wage as Efficiency Wage During the early part of the twentieth century Sydney Webb (1912) argued that paying a minimum wage would have the effect of increasing efficiency because workers would become more efficient. The employer would have incentive to find ways to increase productivity either by getting workers to produce more or by substituting technology for labor. But the worker would also have incentive to improve his or her skills so that the value of his/her labor would justify the new wage. Moreover, workers would also become more productive because a higher wage would better enable them to maintain themselves physically, which in turn would sustain their morale. The more contemporary view of efficiency wages holds that workers who receive higher wages have a stronger incentive to hold onto their jobs because the costs associated with job loss are now higher. Employers also benefit because the costs of higher wages are usually offset by savings in monitoring costs (Shapiro and Stiglitz 1990). Recent studies on the effects of the living wage are confirming some of these positive benefits. Aside from the projected macroeconomic benefits of affording workers greater purchasing power that may also decrease the public assistance roles, there are also the benefits to employers in the form of reduced turnover, lower replacement, recruitment and training costs, and overall increases in productivity (Howes 2002; Fairris 2003; Reich, Hall and Jacobs 2003). 8

11 In a study of the impact of the Los Angles living wage ordinance on employers, David Fairris (2003) found that on the basis of both employee and employer surveys that the starting wages for the largest low-wage occupations increased significantly among living wage establishments than among non-living wage establishments with a difference of roughly $1.70 per hour. And yet higher labor costs were also offset by other savings due to reduced turnover. Among workers in the largest lowest wage occupations, turnover decreased by roughly 50 percent. The average cost of replacing a low wage worker was reported to be $807 in the nonliving wage survey, and this included separation, search, training and lost productivity during the time it took new workers to come up to speed. Therefore, Fairris calculated the per worker saving to be $226 per workers assuming a turnover reduction from 49 to 21 percent. To then pay workers an extra $1.80 per hour would amount to additional $3600 per year for each worker. The savings in turnover costs, then, amounted to roughly six percent of the increased wage bill per worker per year. On the other hand, to the extent that a two-tiered ordinance was designed to encourage low-wage employers to provide health insurance, no change was found in the incidence of employer-paid benefits to affected workers among city service contractors as a result of the living wage ordinance. Similarly, in a study of the impact of the ordinance at San Francisco International Airport, Michael Reich, Peter Hall, and Ken Jacobs (2003) found that despite a significant rise in overall labor costs, there was also a significant decrease in labor turnover. The direct cost of implementing the ordinance, which was also part of a larger Quality Standards Program (QSP) to improve safety and security while also improving the labor market conditions at the airport, was approximately $42.7 million a year. Spillover costs to other workers and employers added 9

12 another $14.9 million to employer costs. And yet, turnover fell by an average of 34 percent among all surveyed firms and 60 percent among those firms where average wages increased 10 percent or more. The greatest reduction in turnover, however, was among airport security screeners. During a fifteen month period after OSP was implemented in April 2000, turnover fell by almost 80 percent from 94.7 percent to 18.7 percent. Every time an average worker has to be replaced, employers have to pay about $4,275 per worker in turnover costs. Therefore, as a function of raising wages, employers ended up saving $6.6 million each year in turnover costs. To the extent that employers experienced reduced turnover costs, they experienced productivity gains. Total observed wages increased by $56.6 million in annual wages for ground-based nonmanagement employees. Many reported that the quality of work increased, and many workers themselves indicated that they were more inclined to put more effort into their work. Reich et. al. suggest yet another benefit that might accrue to the living wage. That is, to the degree that living wage ordinances reduce worker turnover, they may also provide an additional contribution to unionization. It becomes easier for unions to organize when there is a stable workforce. Higher wages may increase the value of job security, seniority and the other benefits attendant to unionization, and the living wage at San Francisco International appeared to have provided benefit to union organizing when workers were directly involved in the campaigns themselves and when contact with workers was made prior to the ordinance. Although overall employment costs did rise, Reich et. al. maintain that most of the increased costs were absorbed by the airlines. And even had the unlikely assumption been made that there were no offsetting productivity increases and that 100 percent of both direct and indirect costs were passed through to consumers, the costs per worker amounts to a modest cost 10

13 of $1.42 per airline customer. Moreover, there were no significant reductions in employment, rather employment at the airport actually increased by around 15.6 percent during the period in which QSP was implemented. But this may have had more to do with the unique attributes of the airline labor market, and also that a new international terminal had been opened. When added to some of the earlier literature, albeit it was prospective, that estimated that costs to cities of passing ordinances in terms of bidding and service contracts would be low (Pollin and Luce 1998; Nissen with Cattan 1998; Benner and Rosner 1998; Zabin, Reich and Hall 1999; Reynolds, Pearson and Vortkampf 1999), there are clearly reasons to believe that living wages do make for good public policy. Moreover, the demonstrated benefits thus far may well serve to garner essential political support. Still, it does not clue us into the characteristics of those cities that have passed them. At issue are the specific characteristics of a city s labor market that would predispose it to passage of such an ordinance. In the remainder of this paper, I look at the labor markets of selected cities that have passed these ordinances in an attempt to understand just what some of those characteristics may be. Comparative City Analysis Data for this study is drawn from the March CPS files for 1993 and During that nine year period, beginning with Baltimore in 1994, over eighty municipalities enacted living wage ordinances of one type or another. Though some are more encompassing than others, basic ordinances require that firms contracting with municipalities to perform municipal services pay their workers a specified minimum wage. Living wage ordinances are not city wide minima, and are thus limited in overall coverage. Hence the limited coverage also speaks to the limitations of 11

14 the CPS as a tool for analysis. Collected by the U.S. Census Bureau, the March annual supplements contain data on family characteristics, household composition, sources of income, industry and occupation of the job held the longest during the year, weeks worked, poverty and other demographic variables. My purpose is not to look at specifically living wage workers, or those that necessarily fall into living wage contours, but to compare those cities that passed ordinances to those that have not on the basis of their respective labor market characteristics. I specifically look at about 30 cities across different regions that passed ordinances and compare them to about 30, also across different regions, that did not that are similar in size and similar in terms of industrial and occupational characteristics. Although there are limitations to this approach, as cities do each have their unique features than cannot easily be quantified, it nonetheless reveals clear differences that also clue us into some of the demographic differences that would more likely predispose some cities towards passing ordinances over others. These differences, are primarily in industry and occupational composition, educational attainment, and workers place of birth. Nevertheless, because the basic unit of analysis is the individual, I only include those individuals who are of working age, ranging from years of age. 12

15 Table I Ratio of Top and Bottom Fifths of Families in Living Wage Cities, Non-Living Wage Cities and the U.S.* Average Income Average Income Top-to Average Income Average Income Top-to- Difference Percent of Bottom Fifth Top Fifth of bottom of Bottom Fifth of Top Fifth bottom Change of Families of Families Ratio of Families of Families Ratio Cities that Passed Living Wage Ordinances $7466 $ $11311 $ Cities that didn t pass ordinances $9165 $ $13541 $ Entire Country** $8388 $ $11822 $ Percent Change in Quintile Bottom Top LWO NLWO All * While the unit of analysis in the CPS is the individual, family income represents the total family income of which individuals are a part of. So when comparing the top fifth to the bottom fifth in 1993, for example, this means then is that 20 percent of individuals in this sample are in families where the average total income was $7466 compared to another 20 percent who were in families where the average total income was $ Family incomes are in real terms for each sample year. ** Entire Country refers to all individuals in the Annual March CPS sample, whereas the other two categories refer to only those individuals in each category s respective 30 cities. 13

16 Among the first features to stand out is that income inequality in cities that passed ordinances was certainly higher in 1993 prior to passage of the first ordinance. And as Table 1 suggests, passage of ordinances in these cities by no means reversed the trend. Though it cannot be concluded that living wages reduced income inequality over those cities that did not pass them, it would nonetheless appear that percentage growth was less in those cities that passed them over those that did not. Whereas family income inequality rose by percent between in cities that did not pass ordinances and by percent in the rest of the country, it only rose by percent in those cities that did pass ordinances. One reason for greater income inequality in living wage cities over non-living wage cities may be that the living wage cities are simply larger and thus have larger and more diversified populations. There is some literature on cross city comparisons to suggest that a key factor affecting income variation across cities is city size itself. This is generally referred to as the Positive Correlation Thesis. James Long, David Rasmussen and Charles Haworth (1977), for instance, hold that increasing income inequality over time may be a function of increasing population. Stephen Nord (1980), argues that incomes should be distributed more equally in mid size cities. Income inequality can be expected to exhibit a U shaped trend over increasing city sizes, with smaller and larger cities experiencing greater measures of inequality. On the one hand, this may be because large cities tend to attract low-skilled workers. On the other hand, larger and more densely populated areas might imply rising living costs which tend to be compensated for by employers in the form of higher wages. It is also possible that the higher wages found in larger cities may also attract poor migrants who believe that is where their economic opportunities lie. Moreover, larger cities tend to be where the more highly paid 14

17 workers with advanced technical and professional skills tend to gravitate. Meanwhile, Andreas Stich (1999), on the basis of data from the German Socioeconomic Panel (GSOEP) for the years , found that the Gini index showed income inequality to not be dependent on city size in Germany. Comparing West German cities to East German cities, there was little evidence for the positive correlation hypothesis in the West, and in the East, there was no relationship at all between inequality and city size. In terms of population, cities that passed ordinances have a larger average size than those cities that did not, which might only add support to the Positive Correlation Hypothesis. Size, however, does not speak to the specific population demographics or the extent to which there is diversity in that population. Even though there was more income inequality in those cities that passed ordinances, and it remained high in 2002 even after ordinances were passed, the rate of growth did decline in those cities. The lower rate of growth in income inequality would appear to be accounted for in part by the higher percentage increase in average family income among those in the bottom Quintile in those cities that passed ordinances. Still, it remains unclear as to whether this higher percentage increase is in any way attributable to the living wage ordinances. Nevertheless, it is revealing that income inequality is by all appearances a bigger problem in those cities that passed ordinances. And it also appears to be an important characteristic of those cities respective labor markets. Although this does not account for family size, my purpose is simply to present a general overview of income inequality. Moreover, there is little reason to believe that family size is going to vary much across cities. As to whether this is a key determining factor is, it is of course, an open question. There are certainly other factors that need to be explored. 15

18 Table 2 Selected Comparative Demographics between Living Wage Cities and Non Living Wage Cities Living Wage Non-Living Living Wage Non-Living Cities Wage Cities Cities Wage Cities Origin Mexican American Chicano Mexican (Mexicano) Puerto Rican Cuban Central or South American Other Spanish All Other Don t Know NA Education 11 th Grade or less th grade, no diploma High School graduate Some College, no degree Associate Degree B.A. degree Graduate or Professional degree All statistically significant at the 5 percent confidence level. 16

19 In looking at the demographic profile of a community s labor market, there are several factors that need to be addressed such as educational attainment, age of working population, family income levels, gender, race, origin of birth, and composition of both industry and occupation. In comparing living wage cities to non-living wage cities, however, there are two particular demographic features, as can be seen in Table 2, that are really critical. Racial differences between living wage and non-living wage cities are not very large. In both, the majority are white (79.5 v percent in 1993 and 75.3 v percent in 2002). And in both types of cities blacks were the second largest group (13.9 v percent in 1993 and 17.0 v percent in 2002). Living wage cities, however, did have more Asians and Pacific Islanders (5.3 v. 3.3 percent in 1993 and 7.0 v. 4.9 percent in 2002) than non-living wage cities. But the major difference between these cities lies in the place where people were born and educational attainment. Living wage cities have larger immigrant populations, particularly from Central and South America, than do non-living wage cities. Differences between living wage and non-living wage cities are great among Mexican Americans, Mexicans, Central or South Americans, and Cubans. Meanwhile, those cities that did not pass living wage ordinances have a higher percentage of individuals whose place of origin was someplace else, which, while it would include those born in any other country outside those specifically listed, would also include those born in the United States. Therefore, if there are more immigrants in Living Wage cities, and they are coming with little skills, we might account for the greater inequality in those cities on the basis of Nord s argument that the attraction of individuals with lower skills will result in greater inequality because their increasing numbers effectively suppresses wages at the bottom of the 17

20 distribution. Educational attainment also appears to be higher in those cities that did not pass ordinances than those that did, although the differences between them does narrow between Whereas in percent of individuals in living wage cities had no more than an 11 th grade education compared to 16.3 percent in non-living wage cities, only 19 percent had no more than an 11 th grade education in living wage cities compared to 17 percent in non-living wage cities in The percentages of those who have undergraduate and/or post-graduate degrees is higher in non-living wage cities than living wage cities. Peripherally this would then imply more people in higher paying occupations and industries in those cities that did not pass ordinances than in those cities that did, thereby suggesting that there was perhaps less of a need in those cities for such ordinances. Or to state it the other way, to the extent that there is a relationship between low educational attainment and low wage levels, or the greater probability of being employed in low wage occupations, we might infer that the wages of those at the bottom are lower in those cities that passed ordinances because educational levels are lower, thereby implying a greater need for labor market institutions. On this question, Barry Chiswick (1991) suggests that there is a correlation between low earnings, a dearth of skills and language ability. Though a growing literature on economic adjustment or economic assimilation of immigrants is focused on human capital, one important aspect of human capital is actually language capital. Immigrants who don t know the language may find what he refers to as a language minority enclave, which, while it provides them with a network of support, also limits their training opportunities and job mobility. Immigrants generally come to the United States with very poor English language skills. Nearly 80 percent of 18

21 Mexican men, however, reported that they could not speak English at all, while another 20 percent indicated that they spoke not well. Mexicans also reported very low skills in reading English. To the extent that this is true, it is only a foregone conclusion that Mexicans, at least when they first arrive in the U.S., regardless of whether they are legal or illegal, are only bound to wind up at the bottom of the wage distribution because English reading ability among lowskilled immigrants is related to their overall skill level. As to why these cities would be more likely to attract immigrants, as well as having greater numbers with no more than an 11 th grade education, the data suggests that living wage cities actually have more people working in industries and occupations that on the surface, at least, would not appear to require that much skill. Non-living wage cities have more people employed in Executive, Managerial, and Professional Specialty occupations (11.4 v. 9.4 percent in 1993 and 14.4 v percent in 2002), while living wage cities have more people employed as Machine operators, assemblers, and inspectors (5.0 v. 3.5 percent in 1993 and 3.9 v. 2.9 percent in 2002). In fact, this was the only occupational category in which living wage cities had more people working than non-living wage cities. Living wage cities also have more people employed in both the Durable and Non-Durable goods industries, as well as services. Therefore, to the extent that there is a relationship between low educational attainment and low wage levels, or the greater probability of being employed in low wage occupations, we might infer that the wages of those at the bottom are lower in those cities that passed ordinances because educational levels are lower, thereby implying a greater need for labor market institutions institutions that will boost wages.. In terms of family income, it is interesting to note that a higher percentage of individuals 19

22 in families with incomes of less than $20,000 are in those cities (28.4 percent in 1993 and 30 percent in 2002) that passed ordinances than those that did not (21.8 percent in 1993 and 24 percent in 2002). While the percentage of families with incomes of less than $20,000 did grow in both living wage and non-living wage cites during this time frame, it appears to have grown at a lower rate in living wage cities despite the fact that overall living wage cities continued to have more families with incomes below this threshold. If it could be assumed that those in families whose incomes are between $15,000-19,999 fall within a living wage range, the percentage is greater in living wage cities than non-living wage cities. This, then, returns us to the question of just what is it about those cities that have already passed ordinances that made them more likely to pass them? Data show that living wage cities have higher percentages of ethnic and/or immigrant groups, as well as higher percentages of individuals with no more than an 11 th grade education. It is true that educational attainment and skills are not the same thing, but to the extent that educational attainment does speak to a skills level, are we then to infer that these cities have higher concentrations of individuals with lower skills, in which case their wage rates are lower? And therefore, their need for a wage boost is greater? Is there not a relationship between educational attainment and origin of birth? Do we then infer that low wage rates are the result of a lack of skills and that ethnic and immigrant groups are less likely to possess these skills? In other words, what is the correlation between educational attainment and origin of birth? Do we also infer that those cities that did not pass ordinances did not do so because the need was not perceived to be as great as evidenced by a higher average family income among the bottom in the those cities that did not pass ordinances? As living wage cities have larger immigrant populations as well as more people with no 20

23 more than an 11 th grade education than non-living wage cities, the question arises as to whether there is a relationship between these two variables. Table 3 compares people s place of birth by educational attainment for both living wage and non-living wage cities. 21

24 Table 3 Educational Attainment by Birth of Origin (Percentages) 11th grade or less 12th grade, no diploma H.S. Graduate Some College Associate Degree B.A. Graduate or Professional 1993 LW NLW LW NLW LW NLW LW NLW LW NLW LW NLW LW NLW Mexican American Chicano Mexican Puerto Rican Cuban Central or South American All Other DK NA Mexican Americans Chicano Mexican l

25 Puerto Rican Cuban Central or South American Other Spanish All Other DK NA Statistically significant at the 5 percent confidence level 23

26 Relative to their presence in the general population, a disproportionate percentage of Mexicans have attained no more than an 11 th grade education. And this is true in both living wage and nonliving wage cities (69.5 percent v percent in living wage cities and 70.2 percent v percent in 1993 in non-living wage cities). Even though there is a decline from , the percentage of Mexicans with no more than an 11 th grade education still remains high relative to the overall population (52.7 percent v. 19 percent in living wage cities and 58.2 percent v. 17 percent in non living wage cities). Mexicans are only followed by Puerto Ricans and those from central and South American countries. Although the all other category is largely undefined, it presumably includes those born in countries other than those specifically south of the American border. This, of course, would imply, that it includes those who were born in the United States, Europe, Africa, Asia, and the Middle East. What is important about this particular category is that compared to the others, it has the lowest percentage of individuals with no more than an 11 th grade education in both living wage and non-living wage cities. It also has among the highest percentages of individuals who have attained a B.A. degree and beyond, including a graduate and/or professional degree. Overall comparisons between those cities that passed living wage ordinances and those that did not, then, lead one to believe that cities with both higher percentages of individuals with no more than an 11 th grade education, coupled with higher concentrations of ethnic and/or immigrant groups, particularly south of the American border, are more likely to pass ordinances. They may also be more likely to be in the first quintile, which for the purposes of analysis in this paper will also serve as a proxy for being at the bottom end of the wage distribution. At best, we can speculate that those most likely to fall into the first quintile of the income distribution and 24

27 those who fall into a living wage income range are most likely to derive benefit from living wage ordinances because 1) there may be wage contour effects, and 2) it is a population amenable to being organized by the typical living wage campaign. Because they lack the educational attainment necessary to command higher wages, they are effectively consigned to the low-wage economy. This would also appear to be consistent with much of the general literature on immigration generally, and that focusing on Mexicans and Central Americans more specifically. That is, the general economic situation of immigrants starts off badly but improves as the newcomer learns the ropes, acquires the requisite skills, and otherwise discovers how to send employers the right signals. While Mexican immigrants who came in the 1960s consistently moved ahead from decade to decade, the cohort that came in the 1970s did not fare as well. The earnings of Mexicans and Central Americans particularly lagged behind those of native born workers. But they also had lower per capita income and more children growing up in poverty because their households do contain lots of children (Waldinger 1999). Significant differences in recent cohorts of Mexican immigrants relative to other groups also suggest that they indeed possess fewer skills than those that arrived from Mexico previously. Recent immigrants from Mexico are more likely to come from the lower end of the educational and income distribution, and that patterns of low earnings among Mexican immigrants can be partly explained by lower skill and educational levels among recent arrivals relative to Mexicans born in the United States (Padilla and Glick 2000). Moreover, there appears to be a selfreinforcing engine at work. Lower-skilled immigrants not only find low-level jobs, but the structure of their employment is such that it also limits the rewards to additional gains in employment. According to Waldinger and Gilbertson (1994), whereas foreign born Mexican men 25

28 only gained a 1.1 increment in occupational status for each additional year of schooling, the increment for the same schooling was 4.6 for native whites born to native born parents. In other words, it may be possible to conjecture that it is the composition of the bottom quintiles in terms of educational attainment and place of birth that affect whether a city is more likely to pass living wage ordinances. And to the extent that typical living wage campaigns are about organizing workers, particularly those at the bottom of the distribution, the correlation between these variables may provide some important clues to understanding why some cities may be more predisposed to adopting these ordinances. Consequently, there are a couple of different issues that need to be sorted through. First, there is the issue of what factors are most likely to predispose one to either being in the first quintile or falling within a living wage income range, neither of which is mutually exclusive. The second has to do with those factors likely to result in their passing ordinances. To determine which variables are more likely to predispose one to either being in the first quintile of falling within a living wage income range, I performed a logistical regression analysis on these dependent variables. Since living wage cities appear to have larger immigrant populations, most specifically Mexicans, it makes sense to test for the effects of being Mexican on both the likelihood of falling into a living wage category and being in the first quintile. But on the assumption that the category of other nationalities will include both those from regions other than Spanish speaking countries in Central America and those born in the United States, that needs to be tested as well. Arguably a key factor for why living wage cities may have higher concentrations in both these categories is race, but the data revealed there to be no significant differences between living wage and non-living wage cities on the basis of race. Nevertheless, I 26

29 test for the effects of race as a control against immigrant status. The control against both race and immigrant status is educational attainment, specifically a low educational attainment. All these factors may account for why income inequality appears to be greater in living wage cities over non-living wage cities, but the composition of industry and occupation may also be important factors as well. Living wage cities appear to have more people working in both durable goods and non-durable goods than do non-living wage cities. Moreover, they also appear to have more people working as machine operators, assemblers and inspectors, as well as handlers, equipment cleaners, helpers and laborers than do non-living wage cities. And on even closer inspection, it appears that Mexicans in particular are more likely to be in precisely those occupational categories. Another important difference is that living wage cities have higher percentages of individuals who have never worked than do non-living wage cities. Hence this is a factor that needs to be tested as well. The larger point, however, is that because living wage cities appear to have more such jobs, might that be a reason why Mexicans are gravitating towards them. The purpose of the logistical regression analysis is to determine which variables are more likely to predispose one to either being in the first quintile or falling within a living wage family income range. Based on the size of the coefficients, they can establish which variables are likely to have greater effect. In other words, all they establish are probabilities; they do not establish how much more likely one is to fall into either category based on the size of the coefficient. The analysis is specifically designed to determine which variables have a greater effect, or which ones have a greater probability in resulting in something. It is not to determine how much more of the dependent variable can result given say an increase in units of a particular independent variable. And this is the reason that no continuous variables have been included in the analysis. In the 27

30 pages that follow, I present three sets of regression coefficients, which when considered within the context of a taxonomy, show some powerful effects. The first set of coefficients simply tests for those variables that are more likely to predispose one to either fall within a living wage family income range or the first quintile. The second then takes those variables that have relatively positive effects for falling within either and tests whether they may account for who is perhaps more likely to live in a city that passed a living wage ordinance. The second set of coefficients demonstrate that Mexicans, in particular, as well as those with no more than an 11 th grade education, are more likely to live in living wage cities. Consequently, the third set tests the effects of such variables against the likelihood of working either in the occupational categories of machine operators, assemblers and inspectors; and handlers, equipment cleaners, helpers and laborers; or the industrial categories of both durable and non-durable goods. The reason for this third set is specifically because both of these occupational and industrial categories had higher percentages of individuals in living wage cities than non-living wage cities. All equations are based on each variable being set to a value of 1. Each table will present the logit coefficients, with their statistical significance directly below in parenthesis. The independent variables are as follows: LOED = Those with no more than an 11 th grade educational attainment. HIGHSCH = Those who have graduated High School with a diploma. MALE = men WHITE = Caucasians BLACK = blacks/african Americans MEXICAN = Those indicating a Mexican origin of birth 28

31 OTHERNA = Those whose place of birth was in the category of other nationalities NEVERWORK = Those who indicated that they never worked. HANDLER = Those working as either handlers, equipment cleaners, helpers and laborers. DURGOOD = Those working in durable goods industry LVWGCITY = Those living in a city that passed an ordinance (in 1993 those that lived in a city that would pass such an ordinance). LIVWGINC = Those living in families whose family incomes fall within what might be considered a living wage range. FIRSTQOR = Those in the first quintile living in cities that passed living wage ordinances. Table 4 Logistical Regression Coefficients First Set Living Wage Income Range First Quintile HANDLER (.000)* (.000) (.000) (.000) MALE (.076) (.001) (.000) (.000) BLACK (.000) (.000) (.000) (.000) LOED (.000) (.000) (.000) (.000) MEXICAN (.000) (.000) (.000) (.000) OTHERNA (.001) (.005) (.000) (.000) DURGOOD (.123) (.689) (.000) (.000) NEVERWORK